The space environment contains numerous high-energy particles, and a single high-energy particle passing through a spacecraft shell bombards the electronic devices within, triggering single-particle effects such as device logic state upset and function failures, which, in turn, affect spacecraft operation reliability and mission accomplishment.

Notably, ground accelerator irradiation tests provide an important and effective means for simulating space single event effects and for predicting the risks of single event effect rates for electronic devices in space applications. Generally, electronic devices can be used in spacecraft only if their resistance radiation indicators meet astronautical application requirements.

Spacecraft are typically exposed to space radiation particles, primarily heavy ions and protons; therefore, single event effect simulation testing for electronic devices relies predominantly on heavy ion and proton accelerators. To address the requirements of single event effect testing, technologies such as large-area beam expansion and homogenization, high-precision beam current diagnosis, and efficient test terminals have been developed to fulfill the requirements of various test tasks.

Particular focus is placed on the CIAE's (China Institute of Atomic Energy) heavy ion single event and proton single event effect simulation test techniques and the heavy ion microbeam technique for radiation sensitive area identification for electronic devices. Subsequently, the aforementioned techniques are applied to a single event effect risk evaluation for astronautical electronic devices.

In the future, the demand for radiation-resistant devices is expected to continue to increase in the aerospace, nuclear industry, and other radiation application fields. It is, therefore, necessary to further exploit the irradiation potential of existing domestic single event effect simulation equipment and establish new accelerator platforms with improved capacity for single event effect simulation testing.

Machine learning, which has been widely applied to scientific research in recent years, can be used to investigate the inherent correlations within a large number of complex data.

We evaluate the performances of two types of machine-learning algorithms for correcting nuclear mass models, reconstructing the impact parameter in heavy-ion collisions, and extracting the symmetry energy slope parameter. We also discuss the extrapolation and generalization ability of the machine-learning models.

For correcting the nuclear mass models, 10 characteristic quantities are fed into the LightGBM to mimic the residual between the experimental and the theoretical binding energies. For impact parameter or symmetry energy, two types of observables constructed based on the particle information simulated by using the UrQMD transport model for setting up the different impact parameters or symmetry energy slope parameters are used as inputs to a conventional neural network and the LightGBM to extract the original information.

Analysis of these nuclear physics problems reveals the potential applicability of machine-learning methods.

Machine-learning methods can be used to investigate new physical problems, thereby promoting the development of both theory and experiment.

To date, various nuclides up to Z = 118 have been discovered and synthesized, raising the challenge of synthesizing nuclides with Z ≥ 119. Recently, the fusion-evaporation reactions 243Am54Cr,xn119297-x and 243Am55Mn,xn120298-x have been suggested as methods for synthesizing new elements with Z = 119 and 120. As α-decay is a powerful tool for the identification of new elements or nuclides, accurate predictions of the α-decay properties of the reaction products could be a useful reference for future experiments.

This study aims to provide quantitative predictions of the α-decay, spontaneous fission, and β-decay half-lives for the α-decay chains of ^{293, 294}119 and ^{294, 295}120 and to demonstrate the competition between the decay modes for these nuclei.

An improved density-dependent cluster model (DDCM+) is used to calculate the α-decay half-lives, taking the anisotropy of the surface diffuseness into account. The spontaneous fission half-lives are calculated using the Karpov formula, which is related to the fissility parameter and fission barrier height of the potential energy surface. The β-decay half-lives are determined using a finite-range droplet model (FRDM).

The predictive α-decay half-lives for the α-decay chains of ^{293, 294}119 and ^{294, 295}120 are obtained using the DDCM+ model, and the theoretical half-lives of the spontaneous fission and β-decay for these nuclides are also presented.

For the α-decay chains of ^{293, 294}119 and ^{294, 295}120, α-decay is predicted to be the dominant decay mode for most of the nuclei, while the half-lives of spontaneous fission and β-decay are predicted to be comparable to those of the α-decay near the region of A = 261. We expect that these results will serve as a useful reference for the synthesis of new isotopes in the future.

β-decay half-life is one of the fundamental physical properties of unstable nuclei and plays an important role in nuclear physics and astrophysics.

This study aimed to provide accurate nuclear β-decay half-life predictions and reasonable uncertainties associated with the predictions.

Nuclear β-decay half-lives were studied based on the Bayesian neural network (BNN) approach. Three types of neural networks with x = (Z, N), x = (Z, N, Q_β), and x = (Z, N, δ, Q_β) were constructed as inputs to explore the influence of the input on the prediction. The posterior distributions were sampled using the Markov chain Monte Carlo algorithm. The mathematical expectations and standard deviations of the neural network predictions on the posterior distributions were used as the predicted values and errors of the BNN approach.

The learning accuracy can be significantly improved by incorporating the β-decay energy and physical quantity related to the nuclear pair effect into the neural network input layer and then using the logarithm of β-decay half-life as the network output. For nuclei with half-lives of less than 1 s, the prediction accuracy is approximately 0.2 orders of magnitude, which is similar to that afforded by the BNN method by learning the differences between the logarithms of the experimental half-lives and theoretical results.

The Bayesian neural network can accurately predict β-decay half-lives. When extrapolated to the unknown nuclear region, the predicted β-decay half-lives agree with the results of other theoretical models within errors, especially for nuclei with Z ? 50.

Isoscalar pairing plays an important role in the spin-isospin excitation of nuclei. The discovery of super Gamow-Teller (GT) states in N≈Z nuclei has motivated researchers to explore the effects of isoscalar pairing on spin-isospin excitations.

This study aims to investigate the effects of the isoscalar pairing interaction on GT and spin-dipole (SD) transitions in ⁴²Ca.

By solving the relativistic Hartree-Bogoliubov equation, we obtained the canonical single-nucleon basis and occupation amplitudes, which were used as inputs for the quasiparticle phase-random approximation (QRPA) calculation. Using the QRPA model, the GT and SD transitions in ⁴²Ca were calculated, where the Gaussian isoscalar pairing force was adopted, with its strength being a free parameter.

For GT states, the isoscalar pairing mixed the spin-flip transition configuration into the low-lying GT state, enhancing the collectivity of the low-energy GT state and significantly increasing its transition strength. Meanwhile, the isoscalar pairing force induced a shift of the low-energy GT state toward lower energies owing to the attractive properties of the isoscalar pairing force. For SD states, the isoscalar pairing force hardly affected the strengths and energies of SD states in ⁴²Ca.

Isoscalar pairing force was essential for restoring the SU(4) symmetry and hence reproducing the low-energy super GT state of ⁴²Ca in the experiment, whereas it hardly affected the SD states.